Apple Rootstock Cutting Drought-Stress-Monitoring Model Based on IMYOLOv11n-Seg
To ensure the normal water status of apple rootstock softwood cuttings during the initial stage of cutting, a drought stress monitoring model was designed. The model is optimized based on the YOLOv11n-seg instance segmentation model, using the leaf curl degree of cuttings as the classification basis...
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MDPI AG
2025-07-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/15/15/1598 |
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| author | Xu Wang Hongjie Liu Pengfei Wang Long Gao Xin Yang |
| author_facet | Xu Wang Hongjie Liu Pengfei Wang Long Gao Xin Yang |
| author_sort | Xu Wang |
| collection | DOAJ |
| description | To ensure the normal water status of apple rootstock softwood cuttings during the initial stage of cutting, a drought stress monitoring model was designed. The model is optimized based on the YOLOv11n-seg instance segmentation model, using the leaf curl degree of cuttings as the classification basis for drought-stress grades. The backbone structure of the IMYOLOv11n-seg model is improved by the C3K2_CMUNeXt module and the multi-head self-attention (MHSA) mechanism module. The neck part is optimized by the KFHA module (Kalman filter and Hungarian algorithm model), and the head part enhances post-processing effects through HIoU-SD (hierarchical IoU–spatial distance filtering algorithm). The IMYOLOv11-seg model achieves an average inference speed of 33.53 FPS (frames per second) and the mean intersection over union (MIoU) value of 0.927. The average recognition accuracies for cuttings under normal water status, mild drought stress, moderate drought stress, and severe drought stress are 94.39%, 93.27%, 94.31%, and 94.71%, respectively. The IMYOLOv11n-seg model demonstrates the best comprehensive performance in ablation and comparative experiments. The automatic humidification system equipped with the IMYOLOv11n-seg model saves 6.14% more water than the labor group. This study provides a design approach for an automatic humidification system in protected agriculture during apple rootstock cutting propagation. |
| format | Article |
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| institution | Kabale University |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Agriculture |
| spelling | doaj-art-63a7e52180d9487597d9f6e008541a552025-08-20T03:36:30ZengMDPI AGAgriculture2077-04722025-07-011515159810.3390/agriculture15151598Apple Rootstock Cutting Drought-Stress-Monitoring Model Based on IMYOLOv11n-SegXu Wang0Hongjie Liu1Pengfei Wang2Long Gao3Xin Yang4College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, ChinaCollege of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, ChinaCollege of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, ChinaCollege of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, ChinaCollege of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, ChinaTo ensure the normal water status of apple rootstock softwood cuttings during the initial stage of cutting, a drought stress monitoring model was designed. The model is optimized based on the YOLOv11n-seg instance segmentation model, using the leaf curl degree of cuttings as the classification basis for drought-stress grades. The backbone structure of the IMYOLOv11n-seg model is improved by the C3K2_CMUNeXt module and the multi-head self-attention (MHSA) mechanism module. The neck part is optimized by the KFHA module (Kalman filter and Hungarian algorithm model), and the head part enhances post-processing effects through HIoU-SD (hierarchical IoU–spatial distance filtering algorithm). The IMYOLOv11-seg model achieves an average inference speed of 33.53 FPS (frames per second) and the mean intersection over union (MIoU) value of 0.927. The average recognition accuracies for cuttings under normal water status, mild drought stress, moderate drought stress, and severe drought stress are 94.39%, 93.27%, 94.31%, and 94.71%, respectively. The IMYOLOv11n-seg model demonstrates the best comprehensive performance in ablation and comparative experiments. The automatic humidification system equipped with the IMYOLOv11n-seg model saves 6.14% more water than the labor group. This study provides a design approach for an automatic humidification system in protected agriculture during apple rootstock cutting propagation.https://www.mdpi.com/2077-0472/15/15/1598YOLOv11n-seginstance segmentationcuttingsleaf curl degreedrought-stress grade |
| spellingShingle | Xu Wang Hongjie Liu Pengfei Wang Long Gao Xin Yang Apple Rootstock Cutting Drought-Stress-Monitoring Model Based on IMYOLOv11n-Seg Agriculture YOLOv11n-seg instance segmentation cuttings leaf curl degree drought-stress grade |
| title | Apple Rootstock Cutting Drought-Stress-Monitoring Model Based on IMYOLOv11n-Seg |
| title_full | Apple Rootstock Cutting Drought-Stress-Monitoring Model Based on IMYOLOv11n-Seg |
| title_fullStr | Apple Rootstock Cutting Drought-Stress-Monitoring Model Based on IMYOLOv11n-Seg |
| title_full_unstemmed | Apple Rootstock Cutting Drought-Stress-Monitoring Model Based on IMYOLOv11n-Seg |
| title_short | Apple Rootstock Cutting Drought-Stress-Monitoring Model Based on IMYOLOv11n-Seg |
| title_sort | apple rootstock cutting drought stress monitoring model based on imyolov11n seg |
| topic | YOLOv11n-seg instance segmentation cuttings leaf curl degree drought-stress grade |
| url | https://www.mdpi.com/2077-0472/15/15/1598 |
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